2.4 SFARI Genes
Load R packages
library(tidyverse) ; library(reshape2) ; library(glue) ; library(plotly)
library(RColorBrewer) ; library(viridis) ; require(gridExtra) ; library(GGally) ; library(ggpubr)
library(rstatix)
library(expss)
library(DESeq2)
library(knitr) ; library(kableExtra)Load preprocessed dataset (code in 2.1.Preprocessing_pipeline.Rmd)
# SFARI Genes
SFARI_genes = read_csv('./../../SFARI/Data/SFARI_genes_01-03-2020_w_ensembl_IDs.csv')
# Load Gandal dataset
load('./../Data/preprocessedData/preprocessed_data.RData')
datExpr = datExpr %>% data.frame
# Updates genes_info with SFARI information
genes_info = genes_info %>% left_join(SFARI_genes, by = 'ID') %>%
mutate(gene.score = ifelse(is.na(`gene-score`) & Neuronal==0, 'Others',
ifelse(is.na(`gene-score`), 'Neuronal', `gene-score`))) %>%
mutate(Group = factor(ifelse(gene.score %in% c('Neuronal','Others'), gene.score, 'SFARI'),
levels = c('SFARI', 'Neuronal', 'Others')))
# SFARI palette
SFARI_colour_hue = function(r) {
pal = c('#FF7631','#FFB100','#E8E328','#8CC83F','#62CCA6','#59B9C9','#b3b3b3','#808080','gray','#d9d9d9')[r]
}There are 864 SFARI Genes in the expression dataset (~77%), 16117 of them have a SFARI Gene Score.
Gene count by SFARI score:
table_info = genes_info %>% apply_labels(`gene-score` = 'SFARI Gene Score', syndromic = 'Syndromic Tag',
Neuronal = 'Neuronal Function', gene.score = 'Gene Score') %>%
mutate(syndromic = as.logical(syndromic), Neuronal = as.logical(Neuronal))
cro(table_info$`gene-score`)|  #Total | |
|---|---|
|  SFARI Gene Score | |
| Â Â Â 1Â | 144 |
| Â Â Â 2Â | 205 |
| Â Â Â 3Â | 440 |
|    #Total cases | 789 |
Gene count by Syndromic tag:
cro(table_info$syndromic)|  #Total | |
|---|---|
|  Syndromic Tag | |
| Â Â Â FALSEÂ | 748 |
| Â Â Â TRUEÂ | 116 |
|    #Total cases | 864 |
Neuronal annotations:
1545 genes have neuronal-related annotations, 201 of these, have a SFARI score
cro(table_info$gene.score[genes_info$`gene-score` %in% as.character(c(1:3))],
list(table_info$Neuronal[genes_info$`gene-score` %in% as.character(c(1:3))], total()))|  Neuronal Function |  #Total | |||
|---|---|---|---|---|
| Â FALSEÂ | Â TRUEÂ | Â | ||
|  Gene Score | ||||
| Â Â Â 1Â | 97 | 47 | Â | 144 |
| Â Â Â 2Â | 146 | 59 | Â | 205 |
| Â Â Â 3Â | 345 | 95 | Â | 440 |
|    #Total cases | 588 | 201 |  | 789 |
rm(table_info)2.4.1 Analysis of all SFARI Genes together
Mean Expression
plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr)) %>%
left_join(genes_info, by='ID')
wt = plot_data %>% wilcox_test(MeanExpr~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 1
base = 15.7
pos_y_comparisons = c(base, base+increase, base)
plot_data %>% ggplot(aes(Group, MeanExpr)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .03) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) +
xlab('') + ylab('Mean Expression') + #ggtitle('Mean Expression Comparison') +
scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
'Neuronal' = 'Neuronal\ngenes')) +
theme_minimal() + theme(legend.position='none')
Log Fold Change
The propotion of over and underexpressed genes is very simiar for each group, so we can just focus on the magnitude of the LFC independently of the sign.
genes_info %>% mutate(direction = ifelse(log2FoldChange>0, 'overexpressed', 'underexpressed')) %>%
group_by(Group, direction) %>% tally(name = 'overexpressed') %>%
filter(direction == 'overexpressed') %>% ungroup %>%
left_join(genes_info %>% group_by(Group) %>% tally(name = 'Total'), by = 'Group') %>% ungroup %>%
mutate('underexpressed' = Total - overexpressed ,
'ratio' = round(overexpressed/underexpressed,2)) %>%
dplyr::select(Group, overexpressed, underexpressed, ratio) %>%
kable %>% kable_styling(full_width = F)| Group | overexpressed | underexpressed | ratio |
|---|---|---|---|
| SFARI | 379 | 410 | 0.92 |
| Neuronal | 673 | 671 | 1.00 |
| Others | 7194 | 6790 | 1.06 |
plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr)) %>%
left_join(genes_info, by='ID')
wt = plot_data %>% filter(log2FoldChange>0) %>%
wilcox_test(MeanExpr~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 1
base = 15.7
pos_y_comparisons = c(base, base+increase, base)
p1 = plot_data %>% filter(log2FoldChange>0) %>% ggplot(aes(Group, MeanExpr)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .03) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) +
xlab('') + ylab('Mean Expression') + ggtitle('Overexpressed genes') +
scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
'Neuronal' = 'Neuronal\ngenes')) +
theme_minimal() + theme(legend.position='none')
wt = plot_data %>% filter(log2FoldChange<0) %>%
wilcox_test(MeanExpr~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
p2 = plot_data %>% filter(log2FoldChange<0) %>% ggplot(aes(Group, MeanExpr)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .03) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) +
xlab('') + ylab('Mean Expression') + ggtitle('Underexpressed genes') +
scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
'Neuronal' = 'Neuronal\ngenes')) +
theme_minimal() + theme(legend.position='none')
grid.arrange(p1, p2, nrow=1)
Both with the original LFC values as well as the shrunken ones, the difference in LFC magnitude between SFARI genes and Neuronal genes is statisically significant with a p-value lower than \(10^{-4}\).
wt = genes_info %>% mutate(abs_lfc = abs(log2FoldChange),
Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>%
wilcox_test(abs_lfc~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 0.04
base = 0.45
pos_y_comparisons = c(base, base + increase, base)
p1 = genes_info %>% mutate(Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>%
ggplot(aes(Group, abs(log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .005) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(7,8)))) +
coord_cartesian(ylim= c(0, max(pos_y_comparisons))) +
scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
'Neuronal' = 'Neuronal\ngenes')) +
xlab('') + ylab('Original LFC Magnitude') + theme_minimal() + theme(legend.position='none')
wt = genes_info %>% mutate(abs_lfc = abs(shrunken_log2FoldChange),
Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>%
wilcox_test(abs_lfc~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 0.03
base = 0.35
pos_y_comparisons = c(base, base + increase, base)
p2 = genes_info %>% mutate(Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>%
ggplot(aes(Group, abs(shrunken_log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .01) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(7,8)))) +
coord_cartesian(ylim= c(0, max(pos_y_comparisons))) +
scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
'Neuronal' = 'Neuronal\ngenes')) +
xlab('') + ylab('Shrunken LFC Magnitude') + theme_minimal() + theme(legend.position='none')
grid.arrange(p1, p2, nrow = 1)
rm(increase, base, pos_y_comparisons, wt)Full plots, without cropping out outliers
p1 = ggplotly(genes_info %>% mutate(Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>%
ggplot(aes(Group, abs(log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(7,8)))) +
scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
'Neuronal' = 'Neuronal\ngenes')) +
xlab('') + ylab('Original LFC Magnitude') + theme_minimal() + theme(legend.position='none'))
p2 = ggplotly(genes_info %>% mutate(Group = factor(Group, levels = c('SFARI','Others', 'Neuronal'))) %>%
ggplot(aes(Group, abs(shrunken_log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=Group)) +
scale_fill_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(7,8)))) +
scale_x_discrete(labels=c('SFARI' = 'SFARI\nGenes', 'Others' = 'Other\ngenes',
'Neuronal' = 'Neuronal\ngenes')) +
xlab('') + ylab('Shrunken LFC Magnitude') + theme_minimal() + theme(legend.position='none'))
subplot(p1, p2, nrows = 1)Differential Expression
genes_info %>% group_by(Group, significant) %>% tally(name = 'DE') %>% filter(significant) %>% ungroup %>%
left_join(genes_info %>% group_by(Group) %>% tally(name = 'Total'), by = 'Group') %>% ungroup %>%
mutate('prop_DE' = round(DE/Total,2)) %>% dplyr::select(Group, DE, prop_DE, Total) %>%
kable(caption = 'Proportion of DE Genes by Group') %>% kable_styling(full_width = F)| Group | DE | prop_DE | Total |
|---|---|---|---|
| SFARI | 242 | 0.31 | 789 |
| Neuronal | 482 | 0.36 | 1344 |
| Others | 3749 | 0.27 | 13984 |
lfc_list = seq(0, 0.3, 0.01)
all_counts = data.frame('group'='All', 'n'=as.character(nrow(genes_info)))
Others_counts = data.frame('group'='Others', n=as.character(sum(genes_info$Group == 'Others')))
Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(genes_info$Group == 'Neuronal')))
lfc_counts_all = genes_info %>% filter(Group == 'SFARI') %>% tally %>%
mutate('group'='SFARI', 'n'=as.character(n)) %>%
dplyr::select(group, n) %>%
bind_rows(Neuronal_counts, Others_counts, all_counts) %>%
mutate('lfc'=-1) %>% dplyr::select(lfc, group, n)
for(lfc in lfc_list){
# Recalculate genes_info with the new threshold (p-values change)
DE_genes = results(dds, lfcThreshold=lfc, altHypothesis='greaterAbs') %>% data.frame %>%
mutate('ID' = rownames(.)) %>%
left_join(genes_info %>% dplyr::select(ID, Neuronal, gene.score, Group), by = 'ID') %>%
filter(padj<0.05 & abs(log2FoldChange)>lfc)
# Calculate counts by groups
all_counts = data.frame('group'='All', 'n'=as.character(nrow(DE_genes)))
Others_counts = data.frame('group'='Others', n=as.character(sum(DE_genes$Group == 'Others')))
Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(DE_genes$Group == 'Neuronal')))
lfc_counts = DE_genes %>% filter(Group == 'SFARI') %>% tally %>%
mutate('group'='SFARI', 'n'=as.character(n)) %>%
bind_rows(Neuronal_counts, Others_counts, all_counts) %>%
mutate('lfc'=lfc) %>% dplyr::select(lfc, group, n)
# Update lfc_counts_all
lfc_counts_all = lfc_counts_all %>% bind_rows(lfc_counts)
}
# Add missing entries with 0s
lfc_counts_all = expand.grid('group'=unique(lfc_counts_all$group), 'lfc'=unique(lfc_counts_all$lfc)) %>%
left_join(lfc_counts_all, by=c('group','lfc')) %>% replace(is.na(.), 0)
# Calculate percentage of each group remaining
tot_counts = genes_info %>% filter(Group == 'SFARI') %>% tally() %>%
mutate('group'='SFARI', 'tot'=n) %>% dplyr::select(group, tot) %>%
bind_rows(data.frame('group'='Neuronal', 'tot'=sum(genes_info$Group == 'Neuronal')),
data.frame('group' = 'Others', 'tot' = sum(genes_info$Group == 'Others')),
data.frame('group'='All', 'tot'=nrow(genes_info)))
lfc_counts_all = lfc_counts_all %>% filter(lfc!=-1) %>% #, group!='Others') %>%
left_join(tot_counts, by='group') %>% mutate('perc'=round(100*as.numeric(n)/tot,2))
# Plot change of number of genes
ggplotly(lfc_counts_all %>% filter(group != 'All') %>%
mutate(group = factor(group, levels = c('SFARI', 'Neuronal', 'Others'))) %>%
ggplot(aes(lfc, perc, color=group)) + geom_point(aes(id=n)) + geom_line() +
scale_color_manual(values=c('#00A4F7', SFARI_colour_hue(r=c(8,7)))) +
ylab('Percentage of DE Genes') + xlab('LFC threshold') + labs(color = 'Group') +
#ggtitle('Effect of filtering thresholds in SFARI Genes') +
theme_minimal() + theme(legend.position = 'bottom'))rm(lfc_list, all_counts, Others_counts, Neuronal_counts, lfc_counts, lfc_counts_all, DE_genes, tot_counts)2.4.2 Analysis of SFARI Genes by SFARI Score
Mean Expression
plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr)) %>% left_join(genes_info, by='ID')
wt = plot_data %>% wilcox_test(MeanExpr~gene.score, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 1
base = 15.5
pos_y_comparisons = c(base + c(0,1,3,5)*increase, base+c(0,2,4)*increase, base+0:1*increase, base)
plot_data %>% ggplot(aes(gene.score, MeanExpr)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .01) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) +
xlab('') + ylab('Mean Expression') +
scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3',
'Others' = 'Other\ngenes', 'Neuronal' = 'Neuronal\ngenes')) +
theme_minimal() + theme(legend.position='none')
Log Fold Change
genes_info %>% mutate(direction = ifelse(log2FoldChange>0, 'overexpressed', 'underexpressed')) %>%
group_by(gene.score, direction) %>% tally(name = 'overexpressed') %>%
filter(direction == 'overexpressed') %>% ungroup %>%
left_join(genes_info %>% group_by(gene.score) %>% tally(name = 'Total'), by = 'gene.score') %>% ungroup %>%
mutate('underexpressed' = Total - overexpressed ,
'ratio' = round(overexpressed/underexpressed,2)) %>%
dplyr::select(gene.score, overexpressed, underexpressed, ratio) %>%
kable %>% kable_styling(full_width = F)| gene.score | overexpressed | underexpressed | ratio |
|---|---|---|---|
| 1 | 66 | 78 | 0.85 |
| 2 | 102 | 103 | 0.99 |
| 3 | 211 | 229 | 0.92 |
| Neuronal | 673 | 671 | 1.00 |
| Others | 7194 | 6790 | 1.06 |
plot_data = data.frame('ID'=rownames(datExpr), 'MeanExpr'=rowMeans(datExpr)) %>%
left_join(genes_info, by='ID')
wt = plot_data %>% filter(log2FoldChange>0) %>%
wilcox_test(MeanExpr~gene.score, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 1
base = 15.5
pos_y_comparisons = c(base + c(0,1,3,5)*increase, base+c(0,2,4)*increase, base+0:1*increase, base)
p1 = plot_data %>% filter(log2FoldChange>0) %>% ggplot(aes(gene.score, MeanExpr)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .03) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) +
xlab('') + ylab('Mean Expression') + ggtitle('Overexpressed genes') +
scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3',
'Others' = 'Other\ngenes', 'Neuronal' = 'Neuronal\ngenes')) +
theme_minimal() + theme(legend.position='none')
wt = plot_data %>% filter(log2FoldChange<0) %>%
wilcox_test(MeanExpr~gene.score, p.adjust.method='BH') %>% add_x_position(x = 'group')
p2 = plot_data %>% filter(log2FoldChange<0) %>% ggplot(aes(gene.score, MeanExpr)) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .03) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) +
xlab('') + ylab('Mean Expression') + ggtitle('Underexpressed genes') +
scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3',
'Others' = 'Other\ngenes', 'Neuronal' = 'Neuronal\ngenes')) +
theme_minimal() + theme(legend.position='none')
grid.arrange(p1, p2, nrow=1)
Both with the original LFC values as well as the shrunken ones, the difference in LFC magnitude between SFARI genes and Neuronal genes is statisically significant with a p-value lower than \(10^{-4}\).
wt = genes_info %>% mutate(abs_lfc = abs(log2FoldChange),
Group = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>%
wilcox_test(abs_lfc~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 0.04
base = 0.45
pos_y_comparisons = c(base + c(0,1,3,5)*increase, base+c(0,2,4)*increase, base+0:1*increase, base)
p1 = genes_info %>% mutate(gene.score = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>%
ggplot(aes(gene.score, abs(log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .005) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,7,8))) +
coord_cartesian(ylim= c(0, max(pos_y_comparisons))) +
scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3',
'Neuronal' = 'Neuronal\ngenes','Others' = 'Other\ngenes')) +
xlab('') + ylab('Original LFC Magnitude') + theme_minimal() + theme(legend.position='none')
wt = genes_info %>% mutate(abs_lfc = abs(shrunken_log2FoldChange),
Group = factor(gene.score, levels = c('1','2','3','Others', 'Neuronal'))) %>%
wilcox_test(abs_lfc~Group, p.adjust.method='BH') %>% add_x_position(x = 'group')
increase = 0.03
base = 0.35
pos_y_comparisons = c(base + c(0,1,3,5)*increase, base+c(0,2,4)*increase, base+0:1*increase, base)
p2 = genes_info %>% mutate(gene.score = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>%
ggplot(aes(gene.score, abs(shrunken_log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
stat_pvalue_manual(wt, y.position = pos_y_comparisons, tip.length = .01) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,7,8))) +
coord_cartesian(ylim= c(0, max(pos_y_comparisons))) +
scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3',
'Neuronal' = 'Neuronal\ngenes', 'Others' = 'Other\ngenes')) +
xlab('') + ylab('Shrunken LFC Magnitude') + theme_minimal() + theme(legend.position='none')
grid.arrange(p1, p2, nrow = 1)
rm(increase, base, pos_y_comparisons, wt)Full plots, without cropping out outliers
p1 = genes_info %>% mutate(gene.score = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>%
ggplot(aes(gene.score, abs(log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,7,8))) +
scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3',
'Neuronal' = 'Neuronal\ngenes','Others' = 'Other\ngenes')) +
xlab('') + ylab('Original LFC Magnitude') + theme_minimal() + theme(legend.position='none')
p2 = genes_info %>% mutate(gene.score = factor(gene.score, levels = c('1','2','3','Others','Neuronal'))) %>%
ggplot(aes(gene.score, abs(shrunken_log2FoldChange))) +
geom_boxplot(outlier.colour='#cccccc', outlier.shape='o', outlier.size=3, aes(fill=gene.score)) +
scale_fill_manual(values=SFARI_colour_hue(r=c(1:3,7,8))) +
scale_x_discrete(labels=c('1' = 'SFARI\nScore 1', '2' = 'SFARI\nScore 2', '3' = 'SFARI\nScore 3',
'Neuronal' = 'Neuronal\ngenes', 'Others' = 'Other\ngenes')) +
xlab('') + ylab('Shrunken LFC Magnitude') + theme_minimal() + theme(legend.position='none')
subplot(ggplotly(p1), ggplotly(p2), nrows=1)rm(increase, base, pos_y_comparisons, wt)## Warning in rm(increase, base, pos_y_comparisons, wt): object 'increase' not
## found## Warning in rm(increase, base, pos_y_comparisons, wt): object 'base' not found## Warning in rm(increase, base, pos_y_comparisons, wt): object 'pos_y_comparisons'
## not found## Warning in rm(increase, base, pos_y_comparisons, wt): object 'wt' not foundDifferential Expression
lfc_list = seq(0, 0.3, 0.01)
all_counts = data.frame('group'='All', 'n'=as.character(nrow(genes_info)))
Others_counts = data.frame('group'='Others', n=as.character(sum(genes_info$gene.score == 'Others')))
Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(genes_info$gene.score == 'Neuronal')))
lfc_counts_all = genes_info %>% group_by(`gene-score`) %>% filter(`gene-score` != 'Others') %>% tally %>%
mutate('group'=as.factor(`gene-score`), 'n'=as.character(n)) %>%
dplyr::select(group, n) %>%
bind_rows(Neuronal_counts, Others_counts, all_counts) %>%
mutate('lfc'=-1) %>% dplyr::select(lfc, group, n)
for(lfc in lfc_list){
# Recalculate genes_info with the new threshold (p-values change)
DE_genes = results(dds, lfcThreshold=lfc, altHypothesis='greaterAbs') %>% data.frame %>%
mutate('ID'=rownames(.)) %>%
left_join(genes_info %>% dplyr::select(ID, Neuronal, gene.score), by='ID') %>%
filter(padj<0.05 & abs(log2FoldChange)>lfc)
# Calculate counts by groups
all_counts = data.frame('group'='All', 'n'=as.character(nrow(DE_genes)))
Others_counts = data.frame('group'='Others', n=as.character(sum(DE_genes$gene.score == 'Others')))
Neuronal_counts = data.frame('group'='Neuronal', n=as.character(sum(DE_genes$gene.score == 'Neuronal')))
lfc_counts = DE_genes %>% group_by(gene.score) %>% tally %>%
mutate('group'=gene.score, 'n'=as.character(n)) %>%
bind_rows(Neuronal_counts, all_counts) %>%
mutate('lfc'=lfc) %>% dplyr::select(lfc, group, n)
# Update lfc_counts_all
lfc_counts_all = lfc_counts_all %>% bind_rows(lfc_counts)
}
# Add missing entries with 0s
lfc_counts_all = expand.grid('group'=unique(lfc_counts_all$group), 'lfc'=unique(lfc_counts_all$lfc)) %>%
left_join(lfc_counts_all, by=c('group','lfc')) %>% replace(is.na(.), 0)
# Calculate percentage of each group remaining
tot_counts = genes_info %>% group_by(`gene-score`) %>% tally() %>% filter(`gene-score`!='Others') %>%
mutate('group'=factor(`gene-score`), 'tot'=n) %>% dplyr::select(group, tot) %>%
bind_rows(data.frame('group'='Neuronal', 'tot'=sum(genes_info$gene.score == 'Neuronal')),
data.frame('group'='Others', 'tot'=sum(genes_info$gene.score == 'Others')),
data.frame('group'='All', 'tot'=nrow(genes_info)))
lfc_counts_all = lfc_counts_all %>% filter(lfc!=-1) %>% #, group!='Others') %>%
left_join(tot_counts, by='group') %>% mutate('perc'=round(100*as.numeric(n)/tot,2))
# Plot change of number of genes
ggplotly(lfc_counts_all %>% filter(group != 'All') %>%
mutate(group = ifelse(group %in% c('1','2','3'), paste0('Score ',group), group)) %>%
mutate(group = factor(group, levels = c('Score 1', 'Score 2', 'Score 3', 'Neuronal', 'Others'))) %>%
ggplot(aes(lfc, perc, color=group)) +
geom_point(aes(id=n)) + geom_line() + scale_color_manual(values=SFARI_colour_hue(r=c(1:3,8,7))) +
ylab('Percentage of DE Genes') + xlab('LFC threshold') + labs(color = 'Group') +
#ggtitle('Effect of filtering thresholds by SFARI score') +
#guides(color=guide_legend(nrow=2,byrow=TRUE) + # for breaking the legend into two rows
theme_minimal() + theme(legend.position = 'bottom'))rm(lfc_list, all_counts, Neuronal_counts, lfc_counts_all, lfc, lfc_counts, lfc_counts_all, tot_counts,
lfc_counts_all, Others_counts)Session info
sessionInfo()## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.5 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1
##
## locale:
## [1] LC_CTYPE=en_GB.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB.UTF-8 LC_COLLATE=en_GB.UTF-8
## [5] LC_MONETARY=en_GB.UTF-8 LC_MESSAGES=en_GB.UTF-8
## [7] LC_PAPER=en_GB.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] kableExtra_1.1.0 knitr_1.32
## [3] DESeq2_1.24.0 SummarizedExperiment_1.14.1
## [5] DelayedArray_0.10.0 BiocParallel_1.18.1
## [7] matrixStats_0.56.0 Biobase_2.44.0
## [9] GenomicRanges_1.36.1 GenomeInfoDb_1.20.0
## [11] IRanges_2.18.3 S4Vectors_0.22.1
## [13] BiocGenerics_0.30.0 expss_0.10.2
## [15] rstatix_0.6.0 ggpubr_0.2.5
## [17] magrittr_2.0.1 GGally_1.5.0
## [19] gridExtra_2.3 viridis_0.5.1
## [21] viridisLite_0.3.0 RColorBrewer_1.1-2
## [23] plotly_4.9.2 glue_1.4.2
## [25] reshape2_1.4.4 forcats_0.5.0
## [27] stringr_1.4.0 dplyr_1.0.1
## [29] purrr_0.3.4 readr_1.3.1
## [31] tidyr_1.1.0 tibble_3.0.3
## [33] ggplot2_3.3.2 tidyverse_1.3.0
##
## loaded via a namespace (and not attached):
## [1] colorspace_1.4-1 ggsignif_0.6.0 ellipsis_0.3.1
## [4] rio_0.5.16 htmlTable_1.13.3 XVector_0.24.0
## [7] base64enc_0.1-3 fs_1.5.0 rstudioapi_0.11
## [10] farver_2.0.3 bit64_4.0.5 AnnotationDbi_1.46.1
## [13] fansi_0.4.1 lubridate_1.7.4 xml2_1.2.5
## [16] splines_3.6.3 geneplotter_1.62.0 Formula_1.2-3
## [19] jsonlite_1.7.2 annotate_1.62.0 broom_0.7.0
## [22] cluster_2.1.0 dbplyr_1.4.2 png_0.1-7
## [25] compiler_3.6.3 httr_1.4.2 backports_1.1.8
## [28] assertthat_0.2.1 Matrix_1.2-18 lazyeval_0.2.2
## [31] cli_2.0.2 acepack_1.4.1 htmltools_0.5.1.1
## [34] tools_3.6.3 gtable_0.3.0 GenomeInfoDbData_1.2.1
## [37] Rcpp_1.0.6 carData_3.0-3 cellranger_1.1.0
## [40] vctrs_0.3.2 crosstalk_1.1.0.1 xfun_0.22
## [43] openxlsx_4.1.4 rvest_0.3.5 lifecycle_0.2.0
## [46] XML_3.99-0.3 zlibbioc_1.30.0 scales_1.1.1
## [49] hms_0.5.3 yaml_2.2.1 curl_4.3
## [52] memoise_1.1.0 rpart_4.1-15 RSQLite_2.2.0
## [55] reshape_0.8.8 latticeExtra_0.6-29 stringi_1.5.3
## [58] highr_0.8 genefilter_1.66.0 checkmate_2.0.0
## [61] zip_2.0.4 rlang_0.4.10 pkgconfig_2.0.3
## [64] bitops_1.0-6 evaluate_0.14 lattice_0.20-41
## [67] labeling_0.3 htmlwidgets_1.5.1 bit_4.0.4
## [70] tidyselect_1.1.0 plyr_1.8.6 R6_2.5.0
## [73] generics_0.0.2 Hmisc_4.4-0 DBI_1.1.0
## [76] pillar_1.4.6 haven_2.2.0 foreign_0.8-76
## [79] withr_2.2.0 nnet_7.3-14 survival_3.2-7
## [82] abind_1.4-5 RCurl_1.98-1.2 modelr_0.1.6
## [85] crayon_1.3.4 car_3.0-7 rmarkdown_2.7
## [88] jpeg_0.1-8.1 locfit_1.5-9.4 grid_3.6.3
## [91] readxl_1.3.1 data.table_1.12.8 blob_1.2.1
## [94] webshot_0.5.2 reprex_0.3.0 digest_0.6.27
## [97] xtable_1.8-4 munsell_0.5.0